Passage
A data annotator helps train artificial intelligence systems by labeling data. The data can include images, text, audio, or video. For example, an annotator may draw a box around a car in an image and label it as “vehicle.” In text annotation, a sentence may be labeled as positive, negative, or neutral.
Accuracy is very important in data annotation. If labels are wrong, the AI model may learn incorrect patterns and produce unreliable results. For this reason, many annotation projects include a quality review step. During this step, experienced reviewers check the work of annotators and provide feedback.
Good annotators follow instructions carefully and pay attention to details. They must also read guidelines before starting tasks so they understand what should be labeled and what should not be labeled.
